Superior predictive value of estimated pulse wave velocity for all-cause and cardiovascular disease mortality risk in U.S. general adults

Background Estimated pulse wave velocity (ePWV) has been proposed as a potential approach to estimate carotid-femoral pulse wave velocity. However, the potential of ePWV in predicting all-cause mortality (ACM) and cardiovascular disease mortality (CVM) in the general population is unclear. Methods We conducted a prospective cohort study using the data of 33,930 adults (age ≥ 20 years) from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014 until the end of December 2019. The study outcomes included ACM and CVM. Survey-weighted Cox proportional hazards models were used to assess hazard ratios (HRs) and 95% confidence intervals (CIs) to determine the association between ePWV and ACM and CVM. To further investigate whether ePWV was superior to traditional risk factors in predicting ACM and CVM, comparisons between ePWV and the Framingham Risk Score (FRS) and Pooled Cohort Equations (PCE) models were performed. Integrated Discriminant Improvement (IDI) and Net Reclassification Improvement (NRI) were employed to analyze differences in predictive ability between models. Results The weighted mean age of the 33,930 adults included was 45.2 years, and 50.28% of all participants were men. In the fully adjusted Cox regression model, each 1 m/s increase in ePWV was associated with 50% and 49% increases in the risk of ACM (HR 1.50; 95% CI, 1.45–1.54) and CVM (HR 1.49; 95% CI, 1.41–1.57), respectively. After adjusting for FRS, each 1 m/s increase in ePWV was still associated with 29% (HR 1.29; 95% CI, 1.24–1.34) and 34% (HR 1.34; 95% CI, 1.23–1.45) increases in the risk of ACM and CVM, respectively. The area under the curve (AUC) predicted by ePWV for 10-year ACM and CVM were 0.822 and 0.835, respectively. Compared with the FRS model, the ePWV model improved the predictive value of ACM and CVM by 5.1% and 3.8%, respectively, with no further improvement in event classification. In comparison with the PCE model, the ePWV model’s ability to predict 10-year ACM and CVM was improved by 5.1% and 3.5%, and event classification improvement was improved by 34.5% and 37.4%. Conclusions In the U.S. adults, ePWV is an independent risk factor for ACM and CVM and is independent of traditional risk factors. In the general population aged 20 to 85 years, ePWV has a robust predictive value for the risk of ACM and CVM, superior to the FRS and PCE models. The predictive power of ePWV likely originates from the traditional risk factors incorporated into its calculation, rather than from an indirect association with measured pulse wave velocity. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-024-18071-2.


Supplement
Table S1.Survey-weighted cox proportional hazards results examining the association of ePWV with all-cause and CVD mortality in the general population after multiple imputation of five data sets.

Table S2.
In the survey-weighted multivariate adjusted model 6, blood pressure and age were added as additional adjustments.
Table S3.Threshold-effect analysis on ePW and all-cause and CVD mortality.
Table S4.Threshold-effect analysis on mean blood pressure and all-cause and cause-specific mortality.
Table S5.ePWV values at different ages and blood pressure levels (105-75mmHg).The HRs have been fully adjusted for heart rate, pulse pressure, race, gender, poverty income ratio, body mass index, waist, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, Arthritis, antihypertensives, glucose-lowering drugs, smoking, and drinking.Figure S2.Kaplan-Meier survival curves, by ePWV quartile level, for all-cause mortality.Follow-up was initiated one years after enrollment, and landmark analyses were performed within one year of the start of follow-up.
In the multivariate model the HRs have been fully adjusted for heart rate, pulse pressure, race, gender, poverty income ratio, body mass index, waist, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, Arthritis, antihypertensives, glucose-lowering drugs, smoking, and drinking.

Figure S2 :
Figure S2: Kaplan-Meier survival curves, by ePWV quartile level, for all-cause mortality.Follow-up was initiated one years after enrollment, and landmark analyses were performed within one year of the start of follow-up.

Figure S3 .Figure S4 .
Figure S3.Dose-response relationship between MBP with the risk of all-cause and CVD mortality.MBP, mean blood pressure Figure S4.The ePWV model was compared with the age, age-squared, DBP, SBP, and MBP models in the cohort to determine the predictive value of the 10-year risk of all-cause and CVD mortality.

Figure S5 .
Figure S5.The ePWV model and the age combined with its squared and DBP/SBP, or MBP models were compared in the cohort to determine the predictive value of the 10-year risk of all-cause and CVD mortality.

Figure S6 .
Figure S6.Comparison of 10-year all-cause mortality risk between ePWV and FRS and PEC models.

Figure S3 .
Figure S3.Dose-response relationship between MBP with the risk of all-cause and CVD mortality.MBP, mean blood pressure.

Table S2 . In the survey-weighted multivariate adjusted model 6, blood pressure and age were added as additional adjustments.
Adjust for all variables in model 6 except for blood pressure difference.Systolic and diastolic blood pressures replaced the pulse pressure.2Adjustfor all variables in model 6 except for blood pressure difference.Systolic and diastolic blood pressures replaced the blood pressure difference.Age was added to the model for additional adjustment. 1

Table S3 . Threshold-effect analysis on ePW and all-cause and CVD mortality.
HR has been fully adjusted for the following variables: heart rate, pulse pressure, gender, race, poverty income ratio, body mass index, waist, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, Arthritis, antihypertensives, glucose-lowering drugs, smoking, and drinking.HR has been fully adjusted for the following variables: heart rate, gender, race, poverty income ratio, body mass index, waist, estimated glomerular filtration rate, total cholesterol, high-density lipoprotein cholesterol, cardiovascular diseases, chronic kidney disease, diabetes mellitus, chronic bronchitis, hypertension, Arthritis, antihypertensives, glucose-lowering drugs, smoking, and drinking.